If you’re an intellectually rigorous person, find problem-solving methods intriguing. If you desire to build a steep learning curve, then structuring a career in Data Science is tailor-made for you. Here is the answer to your question ‘Is Data Science hard?’.
Data Science is one of the promising fields which gives insights from structured and unstructured data, using complex algorithms. In simpler words, it is the field of study which uses domain expertise, knowledge of mathematics, and statistics with a tinge of programming skills to extract meaningful insights from data.
Is Data Science hard?
Short answer – Yes.
Long answer –
“Nothing comes for free. Nothing worth doing ever comes easy.”
Data Science is going to be rocket science for any beginner, no doubt about that. But, there is no prerequisite stating that one cannot pursue a career in this field if he doesn’t have a Ph.D. or a complex brain.
Anybody can become a data scientist if he has the passion for it and will to upskill himself suitably. There is no rule that one should possess a doctorate or should belong to a Computer Science background to pursue a career in Data Science.
The only thing that matters is the clear-cut understanding of how the field works, the right focus in embracing the career, and good clarity of how you place yourself in the industry, to grow further. The rest of the prerequisites, or rather say myths involved in it, are nothing but gibberish.
A data scientist must have a good hold on subjects like mathematics, business, and technology. Thriving and excelling in multiple skills would be challenging in the commercial environment, but not impossible. There are many other online resources to learn the required skills to portray as an entry-level data scientist.
Another understated advantage of pursuing this career in case you’re unsure of it would be the opportunity you get to excel in much-required programming and mathematical skills, which can be a great fit for your resume always!
So, the answer would be Yes, Data Science is hard, but if pursued passionately, it can be completely worth it.
Why should you choose a career in data science?
Before breaking a path in any field, one must have a thorough in-depth introspection of his role and the pros, cons involved in it. He should know what he brings to the table and so, good homework beforehand is important.
- Acclaimed as the “Sexiest Job of the 21st century”, Data Science continues to be a highly promising, demanding, and revolutionary technology discipline that only a handful of the crowd can understand, because of the myths and the hype revolving around it.
- But, other than the obvious reasons stated, one should have trenchant clarity about the depth of the subject and the role it holds for the future. This is especially true for those who are just entering the field because of their mismatched expectations. So, for beginners who are keen on exploring the field of Data Science, it is important to understand the versatility of roles it offers and the contributions.
To understand the criterion required in pursuing a Data Science career, one should understand the prerequisites and the depth of the field.
- Even though it is not compulsory to hold a Ph.D. to become a data scientist, coming from a strong educational background is of no harm and always helps.
- Data science is not a stagnant field and is a field involving a lot of evolution day-by-day, so having a curious mind capable of an up-to-date adaptation to the new skills required in breaking the Data Science Technology is an important prerequisite. An open mind capable of engulfing every source of knowledge without frequent exhaustion is a great way to learn.
- Holding a good grip and flair knowledge of mathematical skills like problem-solving, data analytics, statistical methods, Regression Analysis, and programming skills like Python, SQL, R, Basic Database knowledge, and SAS are necessary to pursue a data science career.
- Learning SQL is a fundamental tool in Data Science as it forms the basic structure in extracting data and exhibiting data analytics. Support Vector Machines, Regression, Clustering are some of the must learn machine learning algorithms a data scientist should know about.
- Thanks to tons of online platforms, resources, and certifications present, learning the skills is not a mind-boggling task and can be executed by anyone, even a beginner.
Steps involved in entering the Data Science field
For a detailed and staged approach to pursuing a Data Science career, knowing the versatility and making a choice is an important stage.
- Data scientist, Data Analyst, Marketing Analyst, Business Intelligence Analyst, Machine learning Engineer are some of the many roles involved in choosing a Data Science field.
- Choosing the right role in accordance with prior background and experience is a great approach. For beginners, this would be perplexing. Networking with previously worked people and seeking guidance from them regarding the details of each role is a great way to start.
Deep learning of the role and the other aspects it follows.
- Once you get a clear lucidity of what role you would offer, the next logical step is to completely understand the nature of the role. This doesn’t just include the knowledge of the requirements or the skills the role offers, but also about understanding the scope it has for the future and the complete practicality it brings in applying it.
- For instance, if you choose to become a Machine Learning Engineer, having knowledge and the prerequisites of the job is necessary. But what makes the difference is when you go a step ahead and have a complete understanding and deeper sense of how it would be applied in reality.
- Taking a thorough course regarding the role, sticking to the language and the tools required to pursue is a friendly approach to begin.
- Focusing on problem-solving methods, interpreting solutions, and understanding assumptions, even though the math behind the technique is complex, is a dominant attitude one must have to make groundbreaking progress in this field despite its challenges.
- Along with being technically profound, one must not forget the basics of presentation, such as having good communication and networking skills, which always come in handy.
- Networking with the right people, following the right resources and productive interaction with fellow batchmates is a good approach for beginners.
- Data Science is a relatively new field, having only a bunch of people decrypting their path. So, finding track of the right guidance and mentorship is an understated knack.
- The stage of entering a job and having a basic understanding of model deployment is a must.
- Once you get a hang of the role, you would offer the credentials required in pursuing it. Making your mark in the data science community is important. Active participation in hackathons, industry conferences, and workshops could accomplish this to get an in-hand practical experience and mentorship in the respective field.
- This could also serve as an open gate in giving you a plethora of job insights or direct employment opportunities.
- Data Science graduate programs, boot camps, and online platforms mostly focus on training algorithms and forget the basics of model deployment, which can affect a business organization, if neglected.
- Corresponding to the user’s needs and as a solution to the problems detected, designing or augmenting the existing product as a supervised learning model is necessary.
- Irrespective of whether you would work on a job, structuring a project model is basic. This is when model deployment comes into the picture. Designing a model such that its insights are routinely available and meet the user’s needs is a key factor in deployment.
- This could be executed by understanding the interactions between the end-users and the model’s predictions. Businesses do not give model deployment much importance amongst data scientists. With practice and experience, one can easily learn how to deploy their models to production.
Future of Data Science and its Applications
- Data science has been one of the most trending domains in the 21st century. Though it is new, it is proven to be the most talked-about and intriguing domain, as it is successful in providing a peek into the future!
- It sustains huge chunks of data into the algorithms to spell out possibilities and uncertainty of the future. As it is a multidisciplinary domain involving both machine learning and artificial intelligence, it never ceases to amaze us with its aspects.
- Not to forget the sky ticketing demand for data science professionals in every industry, it is for sure considered as one of the most sought careers recently. From transport to telecommunication, retails, logistics, healthcare education, almost every industry is entitled to Data Science. The demand is estimated to only go up in the future. The market size of Data Science platforms is expected to reach 178 billion USD by 2025. Though it is one of the most in-demand jobs, one of the main reasons people don’t opt for this in the first place is because of the lack of clarity and will to constantly upskill yourself to modern technology.
- Data is the fuel in any emerging or already emerged industry, so incorporating data science techniques into business strategies is sure to impose a tremendous impact on future technologies.
- Since it is a multidisciplinary field, the applications involved in using machine learning and AI algorithms are many. Logistics, fraud detection, the building of necessary applications for various civil purposes are some of the many applications of Data Science.
In a complex and trending field like Data Science, having a practical approach towards problem-solving and exposure is the most important thing. Picking on a problem and having a deeper understanding of not only the solution but also the future application is the best approach to make a career in this field.
Though there is a fear of job risk involved in this field, it is important to understand that this field is designed for constant evolution and one should expect a job evolution in this field. With constant up-gradation of skills and prerequisites, this field promises to deliver a major benefit in learning and practical usage in real-world applications.
Conclusion
Considering all the risks and benefits involved in Data Science, paving your way in making a successful data science career can be strenuous. But, once you walk through the difficult stages and develop a constantly curious mind, then this career might be a boon and can be the most rewarding choice you can make.
Nonetheless, it can be a smart move to choose a career in this field, considering the lucrative advantages it offers and the enormous potential it has to grow in the economic market and transform your professional life!
So, data science is hard in the beginning stages, but once you get the deep learning and clarity about it, it surely can be a great deal in your resume if not your career!
Frequently Asked Questions
1) What is the salary one can expect at the beginning of a Data Science career in India?
The salary insights in this field majorly depend on the sub-domain and the role designated to each individual. The average salary for a beginner in India is 7000 USD (approximately 5 LPA). It varies accordingly with expertise in the field and the countries in which it is prioritized.
2) What are the different sub-domains in the Data Science field?
The different sub-domains in Data Science one should know are:
- Computational methods
- Statistical learning and probability
- Machine learning algorithms
- Bayesian Statistics
- Data-driven inference
3) If Data Science is the most rewarding field, why is there a lesser number of audiences engaging in it?
Data Science is a field in which there are bounds of exploration revolved. The reason behind lesser people choosing it is that of the fear of the job risk involved because of automation. Another reason is that the majority of the people simply don’t understand the depth of the subject and lack clarity.
4)What are the top skills one must possess to break their path in this field?
Possessing strong mathematical and programming skills is a major benefit.
- Data Visualization
- Statistical Analysis
- Machine learning in python
- Data intuition
- Complex problem-solving skills
- Linear algebra and calculus
And programming tools like SQL, MATLAB, R, Python
are the top skills to add on to pursue a successive career in this field.
